Senior Data Architect - Vice President

• Define and evolve the technical architecture and roadmap for Citi's enterprise data lake and analytics platform, ensuring alignment with cloud-native and hybrid-cloud strategies in partnership with enterprise architects.
• Design and develop high-performance, scalable, and secure microservices using Java handling high volume data processing
• Design and develop high-performance, scalable, and secure microservices using Java and Spring Boot, translating complex business requirements into robust technical solutions.
• Lead, mentor, and develop a team of data engineers, fostering a culture of technical excellence, continuous learning, and agile delivery.
• Build and maintain high-scale batch and real-time streaming data pipelines using technologies such as Apache Kafka, Apache Flink, Apache Iceberg, Starburst, and Startree/Apache Pinot.
• Optimize data storage and query performance across enterprise data lakes and lake houses, including Delta Lake, Apache Iceberg, Snowflake, and Databricks environments.
• Collaborate with Data Scientists, AI Researchers, Product Managers, and business leaders to translate analytical requirements into scalable data platform capabilities.
• Evaluate and prototype emerging data technologies and frameworks to keep the platform at the forefront of industry innovation.

• Expert-level proficiency in Java, including deep knowledge of the Spring Framework (Spring Boot, Spring Cloud) and microservices architecture design patterns.
• Hands-on experience building and scaling high-volume distributed data lakes, including modern data orchestration, ingestion, processing, and distribution.
• Deep expertise in big data frameworks including Apache Iceberg, Startree/Apache Pinot, and Hive/HDFS, with a strong grasp of distributed computing principles.
• Practical experience with real-time data streaming technologies, specifically Apache Kafka and Apache Flink, applied in production environments.
• Proficiency in SQL, with experience across cloud platforms (AWS, Azure, or GCP) and containerization technologies including Docker and Kubernetes.
• 6+ more years of professional experience in data engineering, software engineering, or data platform development, including at least 3 years in a technical leadership or lead engineering capacity.
• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related quantitative discipline.

• Programming
  
  Languages:
  
  Expert-level proficiency in SQL (must have) and Java , Pyspark (good to have). Additional expertise in Python is appreciated.
• Additional proficiency in Python to complement Java-based development workflows.
• Working knowledge of Databricks for large-scale data processing and analytics workloads.
• Experience building data infrastructure specifically designed to support machine learning, advanced analytics, or AI applications in production.

• Strategic influence over enterprise-scale data platform architecture, with visibility across business functions and senior stakeholders.
• A hybrid working model offering 3 days in the office and 2 days working remotely, supporting both collaboration and flexibility.
• Access to continuous learning and professional development opportunities to deepen technical expertise and grow leadership capability.
• Opportunities to work alongside Data Scientists, AI Researchers, and Enterprise Architects on cutting-edge data and analytics initiatives.
• Comprehensive wellbeing support, financial benefits, and a work-life balance framework designed for senior professionals.
• Global scale and reach, with the resources and infrastructure of one of the world's leading financial institutions.

Technology

Application…